Threats Tagged 'ssrf'

A joint Tenable-SentinelOne analysis of 93 CVE-actor attribution pairs reveals that both state-sponsored actors and cybercriminals independently converge on the same edge infrastructure. Special thanks to SentinelOne® Incident Readiness & Response for their contributions to this publication. It is the shared attack surface where state-sponsored threat actors and financially motivated criminal groups independently converge — not the province of a single adversary category, and not exclusively a nation-state problem, despite two years of headlines about China-nexus actors targeting Ivanti, Fortinet, and Palo Alto Networks. The data here tells a different and much broader story. One focused on vendors vs CVEs. Key Takeaways Two independent observation systems, Tenable exposure telemetry across thousands of customer containers and SentinelOne DFIR casework across 66 CVEs, converge 79% on the same vendor attack surfaces despite minimal CVE-level overlap. Twelve CVEs in the combined dataset have confirmed multi-nexus attribution: state-sponsored and criminal actors independently exploiting the same vulnerability, across five nexus categories (China, Russia, DPRK, Iran, ransomware). The exposure picture is flatter than the headlines suggest: Fortinet, the vendor most associated with edge-device attacks in the press, sits mid-pack on container-grain exposure (25%) — well behind F5 (54%) and in a tight 10-point band with Check Point, Ivanti, and Citrix. 54% of customer environments running F5 products have at least one exposed, actively-exploited CVE; Citrix customers show the slowest remediation patterns at 461 days median time to patch. Remediation complexity, particularly of high priority CVEs, leads to a statistically significant 24-day remediation gap, leaving large windows of opportunity for attackers. The same product lines get hit again and again: Ivanti EPMM and Ivanti Connect Secure each show a newly exploited CVE roughly every 8.5 to 13 months. Leverage multiple defense-in-depth strategies: patch as quickly as possible, but also minimize the attack surface (feature-set minimization) and run endpoints in protect mode to better stop lateral movement from attacks that gain initial access. The convergence is the story Twelve CVEs in the combined dataset have confirmed multi-nexus attribution: state-sponsored and criminal actors independently exploiting the same vulnerability, across five nexus categories. Four examples illustrate the pattern: CVE Product Actors (Nexus) Significance CVE-2026-15409 SonicWall SMA1000 UTA0533 (unattributed) + INC Ransomware Espionage-to-ransomware succession on an active zero-day CVE-2023-42793 JetBrains TeamCity APT29 (Russia) + Lazarus (DPRK) Two state-sponsored actors from different nations on the same CVE CVE-2024-3400 PAN-OS GlobalProtect UTA0218 (China) + INC Ransomware China-nexus zero-day reused by ransomware operators CVE-2024-24919 Check Point Quantum PurpleHaze (China) + Fox Kitten (Iran) China and Iran independently exploiting the same gateway vulnerability The remaining eight confirmed multi-nexus CVEs span Fortinet, Citrix, Cisco, and Ivanti product lines. State-sponsored actors and ransomware operators are not operating in separate vulnerability ecosystems. They share the same entry points into the same products. The breadth of the convergence, not any single actor's activity, is the finding. That pattern holds across the full combined analysis. Three conclusions emerge: Vendor attack surfaces are the persistent exploitation target. The same eleven vendors (i.e., Fortinet, Citrix, Ivanti, Palo Alto Networks, Cisco, Juniper, VMware, Microsoft, Oracle, CrushFTP, and Meta's React framework ) appear in both observation systems at 79% convergence, and all seven edge-product vendors converge. Serial exploitation timing on Ivanti products shows the vulnerability-to-exploitation pipeline refreshing at 8.5 to 13-month intervals on the same product lines. This is structural, not episodic. Patch…

A Server-Side Request Forgery (SSRF) vulnerability exists in the ONLYOFFICE ownCloud Integration plugin version 9.12. The /apps/onlyoffice/ajax/settings/address endpoint does not sufficiently validate the user-supplied Document Server URL before initiating outbound connections. An authenticated administrator can manipulate the document server parameter to cause the ownCloud server to send arbitrary requests to attacker-controlled destinations, including localhost and internal network hosts. This allows internal network reconnaissance and TCP port scanning based on differences in server responses.

In this article AI workloads are becoming high-value control points Case study 1: LiteLLM gateway compromise Case study 2: RAGFlow compromise Case study 3: Kestra compromise Mitigation and protection guidance MITRE ATT&CK techniques observed References Learn more AI is creating a new layer of enterprise infrastructure. Gateways, retrieval platforms, orchestration services, and containerized runtimes now sit between users, applications, data, and models. These systems concentrate credentials, data access, model connectivity, and execution privileges, making them some of the most powerful components in the AI stack. That concentration of trust is also creating new opportunities for attackers. In recent investigations, Microsoft observed activity targeting three distinct AI workloads: a LiteLLM gateway, a RAGFlow deployment, and a Kestra workflow environment. The intrusion paths varied, but the objectives were strikingly similar. Attackers sought to steal credentials, establish persistence, and monetize compromised compute resources. The individual techniques matter, but the broader pattern matters more. Across these cases, attackers treated AI infrastructure as a control plane where credential theft, host compromise, and downstream data access can converge. As organizations continue to deploy AI systems, these platforms are becoming high value targets that deserve the same security scrutiny as other critical enterprise infrastructure. AI workloads are becoming high-value control points The campaign-level signal extends beyond one product. The targeted workloads served different functions, but each exposed assets that could support follow-on abuse, including model-provider keys, proxy-issued virtual keys, database connection strings, tenant configuration, workflow execution, or host compute. Post-compromise behavior varied by workload role. Defenders should inventory exposed AI management surfaces, restrict administrative access, and monitor for gateway-originated execution and secret access. Three observed compromises across AI workloads AI workload Observed activity Attacker objective LiteLLM Observed attacker activity : Python droppers, runtime secret harvesting, PostgreSQL collection, miner deployment, and persistence activity from the LiteLLM gateway context. Microsoft assessment: Initial access likely occurred through exploitation of the exposed LiteLLM gateway surface, consistent with the vulnerability chain involving CVE-2026-42271 and CVE-2026-48710. Credential theft, backend database access, durable host access, and compute monetization. RAGFlow Observed attacker activity : Possible SSRF-style reconnaissance followed several days later by code execution, application-path modification, and placement of a Python hook in the TenantLLM credential-configuration flow. Public research: Describes multiple RAGFlow execution paths; Microsoft does not attribute this intrusion to a specific vulnerability. Intercept newly configured LLM provider credentials and model metadata. Kestra Observed attacker activity : Workflow-origin shell execution, Docker and container-environment discovery, XMRig deployment, and follow-on data collection. Microsoft assessment: Initial access likely involved exploitation of the exposed Kestra orchestration surface, with CVE-2026-49869 providing relevant public vulnerability context. Secret discovery, container-level access, data collection, and rapid compute monetization. Case study 1: LiteLLM gateway compromise Framework role and affected runtime context LiteLLM is commonly deployed as a proxy or gateway between applications and model providers. In that position, the service may hold or retrieve model-provider keys, LiteLLM master keys, virtual-key records, database connection strings, routing configuration, and tenant policy data. Command execution in the gateway runtime therefore exposed a process context close to AI routing and credential material. Figure 1. LiteLLM gateway compromise – attack chain…

Attackers are obfuscating the IP address 169.254.169.254, used by cloud metadata services, by representing it as hostnames. This technique bypasses simple IP-based blocklists designed to prevent Server Side Request Forgery (SSRF) attacks targeting the cloud metadata service. Dynamic DNS services like 1u.ms enable attackers to create such hostnames on the fly, complicating detection and mitigation efforts.

Bulletin ID: 2026-079-AWS Scope: AWS Content Type: Important (requires attention) Publication Date: 08/12/2026 11:45 AM PDT Description: OpenSearch is a community-driven, open-source search and analytics suite. We identified CVE-2026-18952, a missing input validation issue in the threat intelligence feed parser of the OpenSearch Security Analytics plugin. This issue may allow an authenticated user with the security_analytics_full_access role to perform server-side request forgery (SSRF) and read local files via a crafted URL parameter to the threat intel source configuration endpoint. Impacted Versions: OpenSearch Security Analytics Plugin (open-source, self-managed): - Affected: >= 2.15.0 - Fixed: >= 3.5.0 Amazon OpenSearch Service (AWS Managed): - Affected: Domains running engine versions >= 2.15.0 - Fixed: Addressed via service software update for engine version 3.5. The affected functionality is not enabled in the default service configuration. Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.

Bulletin ID: 2026-056-AWS Scope: AWS Content Type: Important (requires attention) Publication Date: 07/15/2026 11:30 AM PDT Description: Strands Agents is an open-source Python SDK for building and running AI agents. The strands-agents-tools package provides pre-built tools for use with the SDK, including the elasticsearch_memory tool for agent memory storage. We identified CVE-2026-15746, a server-side request forgery (SSRF) issue in the elasticsearch_memory tool. The tool exposed its connection parameters (es_url, cloud_id, api_key) as fields the large language model (LLM) could control through the tool schema. When a caller omitted the api_key parameter, the tool fell back to the operator's ELASTICSEARCH_API_KEY environment variable and sent it to whichever host the LLM specified. A crafted prompt could cause the tool to connect to a threat-actor-controlled server and disclose the operator's Elasticsearch API key in the Authorization header. Impacted versions: < 0.7.0 Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.

Bulletin ID: 2026-054-AWS Scope: AWS Content Type: Important (requires attention) Publication Date: 07/14/2026 13:00 PM PDT Description: AWS HealthLake MCP Server (awslabs.healthlake-mcp-server) is a Model Context Protocol server that enables AI assistants to interact with AWS HealthLake FHIR datastores. We identified CVE-2026-15643, a server-side request forgery in the pagination handling component in AWS awslabs.healthlake-mcp-server before 0.0.14 on all platforms might allow a remote authenticated user to exfiltrate AWS temporary security credentials to an arbitrary endpoint via a crafted next_token parameter. The server does not validate that pagination URLs point back to the expected HealthLake endpoint, allowing an actor to redirect subsequent requests to an actor-controlled server. Impacted versions: < 0.0.14 Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.

Linuxfabrik monitoring_plugins_6.0.0 - SSRF

Nodemailer 9.0.0 - File Read/ SSRF

CVE-2026-49876 is an authenticated server-side request forgery (SSRF) vulnerability in Apache Gravitino's JobManager component. It allows an authenticated user to make server-side HTTP requests to internal network and cloud metadata endpoints by exploiting unvalidated job template URIs. The vulnerability affects Apache Gravitino versions from 1.0.0 through 1.2.1. Upgrading to version 1.3.0 addresses the issue.